UNIT01-DBMS.ppt, Lecture notes of Database Management Systems (DBMS)

Download UNIT01-DBMS.ppt and more Lecture notes Database Management Systems (DBMS) in PDF only on Docsity!

DATABASE MANAGEMENT SYSTEMS

TERM 200809

B. Tech II/IT II Semester

UNITI PPT SLIDES

Text Books: (1) DBMS by Raghu Ramakrishnan

(2) DBMS by Sudarshan and Korth

INDEX

UNIT1 PPT SLIDES

S.NO Module as per Lecture PPT Session planner No Slide NO

1. DBS Application and L1 L1 1 to L1 9 **DBMS Vs File Systems

2. View of DATA L2 L2 1 to L2 7
3. DB Language (DML, DDL) L3 L3 1 to L3 6
4. DB Users and Administrator L4 L4 1 to L4 3
5. Data storage and Querying L5 L5 1 to L5 5
6. DBMS Architecture L6 L6 1 to L6 3**

What Is a DBMS? What Is a DBMS?

• (^) A very large, integrated collection of data.
• (^) Models real-world enterprise.
  • (^) Entities (e.g., students, courses)
  • (^) Relationships (e.g., Madonna is taking

CS564)

• (^) A Database Management System (DBMS) is a

software package designed to store and

manage databases.

Why Use a DBMS? Why Use a DBMS?

• (^) Data independence and efficient access.
• (^) Reduced application development time.
• (^) Data integrity and security.
• (^) Uniform data administration.
• (^) Concurrent access, recovery from crashes.

Files vs. DBMS Files vs. DBMS

• (^) Application must stage large datasets

between main memory and secondary

storage (e.g., buffering, pageoriented

access, 32bit addressing, etc.)

• (^) Special code for different queries
• (^) Must protect data from inconsistency

due to multiple concurrent users

• (^) Crash recovery
• (^) Security and access control

Purpose of Database Systems Purpose of Database Systems

• (^) In the early days, database applications were built directly on top of file systems
• (^) Drawbacks of using file systems to store data:
  • (^) Data redundancy and inconsistency
    • (^) Multiple file formats, duplication of information in different files
  • (^) Difficulty in accessing data
    • (^) Need to write a new program to carry out each new task
  • (^) Data isolation — multiple files and formats
  • (^) Integrity problems
    • (^) Integrity constraints (e.g. account balance > 0) become “buried” in program code rather than being stated explicitly
    • (^) Hard to add new constraints or change existing ones

Levels of Abstraction Levels of Abstraction

• (^) Physical level: describes how a record (e.g., customer) is stored.
• (^) Logical level: describes data stored in database, and the relationships among the data. type customer = record customer_id : string; customer_name : string; customer_street : string; customer_city : string; end ;
• (^) View level: application programs hide details of data types. Views can also hide information (such as an employee’s salary) for security purposes.

Summary Summary

• (^) DBMS used to maintain, query large datasets.
• (^) Benefits include recovery from system crashes,

concurrent access, quick application development,

data integrity and security.

• (^) Levels of abstraction give data independence.
• (^) A DBMS typically has a layered architecture.
• (^) DBAs hold responsible jobs

and are wellpaid! 

• (^) DBMS R&D is one of the broadest,

most exciting areas in CS.

Instances and Schemas Instances and Schemas

• (^) Similar to types and variables in programming

languages

• (^) Schema – the logical structure of the database
  • (^) Example: The database consists of information

about a set of customers and accounts and the

relationship between them)

• (^) Analogous to type information of a variable in a

program

• (^) Physical schema : database design at the

physical level

• (^) Logical schema : database design at the logical

level

Instances and Schemas Instances and Schemas

• (^) Instance – the actual content of the database at a particular point in time - (^) Analogous to the value of a variable
• (^) Physical Data Independence – the ability to modify the physical schema without changing the logical schema - (^) Applications depend on the logical schema - (^) In general, the interfaces between the various levels and components should be well defined so that changes in some parts do not seriously influence others.

Data Models Data Models

• (^) A data model is a collection of concepts for

describing data.

• (^) A schema is a description of a particular

collection of data, using the a given data

model.

• (^) The relational model of data is the most

widely used model today.

• (^) Main concept: relation, basically a table with

rows and columns.

• (^) Every relation has a schema, which describes

the columns, or fields.

Example: University Database Example: University Database

• (^) Conceptual schema:
  • (^) Students(sid: string, name: string, login: string, age: integer, gpa:real)
  • (^) Courses(cid: string, cname:string, credits:integer)
  • (^) Enrolled(sid:string, cid:string, grade:string)
• (^) Physical schema:
  • (^) Relations stored as unordered files.
  • (^) Index on first column of Students.
• (^) External Schema (View):
  • (^) Course_info(cid:string,enrollment:integer)

DATA BASE LANGUAGE DATA BASE LANGUAGE

Data Manipulation Language (DML) Data Manipulation Language (DML)

• (^) Language for accessing and manipulating the

data organized by the appropriate data model

• (^) DML also known as query language
• (^) Two classes of languages
• (^) Procedural – user specifies what data is

required and how to get those data

• (^) Declarative (nonprocedural) – user specifies

what data is required without specifying how to

get those data

• (^) SQL is the most widely used query language

Data Definition Language (DDL) Data Definition Language (DDL)

• (^) Specification notation for defining the database schema Example: create table account ( account_number char (10), branch_name char (10), balance integer )
• (^) DDL compiler generates a set of tables stored in a data dictionary
• (^) Data dictionary contains metadata (i.e., data about data)
  • (^) Database schema
  • (^) Data storage and definition language
    • (^) Specifies the storage structure and access methods used
  • (^) Integrity constraints
    • (^) Domain constraints
    • (^) Referential integrity (e.g. branch_name must correspond to a valid branch in the branch table)
  • (^) Authorization